Loading Data into Amazon Redshift from Amazon S3

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1 Loading Data into Amazon Redshift from Amazon S3 By Danijel Hafner, BI Developer, iolap, Inc. Objective Amazon offers loading data to Redshift either from flat files that are stored in an Amazon S3 bucket or from Amazon DynamoDB table. In this article, I will show you how to load data from your local machine to Amazon Redshift using the Amazon S3 service. Also, for inserting data to Amazon Redshift, I will show you how to use COPY command. Test Overview For this test, I used Part Table with 20 million rows and a size of 2.83 GB. I loaded data first from my local machine to the S3 bucket and then from Amazon S3 into Amazon Redshift. You can find Part Table in Amazon S3 bucket: s3://redshift-demo/tpch/100/part/part.tbl. I used an Amazon Redshift cluster with one extra-large node (XL) with 2TB of compressed storage. This storage amount much more than required for my test but it comes in a minimum one-node package. Listed below is the XL node specification. High Storage Extra Large (XL) DW Node: CPU: 2 virtual cores - Intel Xeon E5 ECU: 4.4 Memory: 15 GiB Storage: 3 HDD with 2TB of local attached storage Preparing Data for Loading There is a couple of things we can do to speed up loading process. Step 1 Store your data in sort order so you don t need to run a VACUUM command. For example, the timestamp column can be a good sort key example when we load files on a daily bases. Also, the delimiter can be the last field in a record.

2 Step 2 Redshift uses a massively parallel processing (MPP) architecture to read and load data in parallel from the S3 bucket so I want to split my large flat file into multiple files to take advantage of parallelism. The COPY command loads the data in parallel from multiple files, dividing the workload among the nodes in the cluster. The number of files should correspond to the number of slices in the cluster. I used one XL compute node with two slices and divided the file into 200 files. All files should be roughly the same size and must share a common prefix for the set. Step 3 Compress the files individually using GZIP or LZOP. I used GZIP and reduced size from 2.83GB to 567MB. This is only file compression. However, Amazon Redshift also has its own column compression methods once the data comes in. After this step, I can upload files to the Amazon S3 bucket. Upload to Amazon S3 For loading to the Amazon S3 bucket I used the S3 console. This Amazon service is used to store and retrieve any amount of data and works very well. It is simple, easy to use and worked without any problems for my tests. This task can be performed with some other tools like S3 browser, Bucket Explorer, S3 Browser for Chrome and so on. 2

3 I created bucket named iolap_part_tbl. Once I have my bucket, I upload all Part files from my computer. For 567MB of 200 files, the load took 7min 23sec. Upload speed was around 1300 KB/sec. 3

4 Loading Redshift Data from Amazon S3 I used the COPY command to load data to Redshift. It is the best way to load large amounts of data from Amazon S3 (or DynamoDB). The COPY command loads data in parallel to each compute node. Before loading, I needed to have my target table already created. To do this I must connect to my cluster. I used SQL Workbench/J, a free SQL client tool. My workbench needed a driver that will enable connections to the cluster. Amazon Redshift supports the following version 8 JDBC and ODBC drivers: JDBC ODBC (versions below or later) o o 32-bit 64-bit Below are the SQL Workbench/J setup connection properties: 4

5 The URL is located in the cluster properties in AWS console: Before loading I created the target table: CREATE TABLE part( P_PartKey int, P_Name varchar(64), P_Mfgr varchar(64), P_Brand varchar(64), P_Type varchar(64), P_Size int, P_Container varchar(64), P_RetailPrice decimal(13, 2), P_Comment varchar(64) ); 5

6 Database explorer in SQL Workbench/J: Syntax of the COPY command: copy <table_name> from 's3://<bucket_name>/<object_prefix>' CREDENTIALS 'aws_access_key_id=<my aws_access key id>; aws_secret_access_key=<my aws secret access key>' [<options>]; My COPY command for loading the Part table: copy part from 's3://iolap_part_tbl/part.tbl' CREDENTIALS 'aws_access_key_id={id};aws_secret_access_key={key}' delimiter ' ' gzip; Statistics for loading 20 million records with one XL node: Execution time: 2 minutes and 28 seconds Rows per second: 135,000 MB per second: 15 This result is very acceptable for a cluster configuration with only one node. Amazon Redshift allows you to scale a XL cluster up to 32 nodes and 8XL clusters up to 100 nodes. The upload speed is not proportional to the number of nodes. Two system tables can be helpful in troubleshooting data load issues: STL_LOAD_ERRORS STL_FILE_SCAN 6

7 Unload Data Like the COPY command for loading data, Amazon Redshift has a command to unload data from Amazon Redshift to Amazon S3. This command will copy data back to Amazon S3 with the number of files equal to the number of slices in the current cluster. Amazon Redshift offers some options for this command like compress to GZIP or choosing delimiter type. The original data will remain in Amazon Redshift. unload ('SELECT * FROM part') to 's3://part_unload/' CREDENTIALS 'aws_access_key_id={id};aws_secret_access_key={key}' delimiter ' ' gzip; Execution time was 2 minutes 25 seconds. Summary The COPY command is simple and very fast once the data is up on Amazon servers. Potential bottlenecks for large data volumes can be uploaded to Amazon S3. I used a cluster with the minimum number of nodes and loading times were very good. More nodes in cluster will result in better performance and it s important to know how many nodes you need based on your business requirements. About the Author Danijel Hafner is a BI Developer at iolap d.o.o (a subsidiary of iolap Inc.) located in Rijeka, Croatia. He graduated from Faculty of Organization and Informatics in Varaždin, Croatia. Danijel has been with iolap since 2010 and has worked on several highly visible projects in the U.S. He specializes in Data Warehousing and ETL development. 7

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